Mahdi Aliyari Shoorehdeli

This paper deals with the design of adaptive fuzzy dynamic surface control for uncertain strict-feedback nonlinear systems with asymmetric time-varying output constraints in the presence of input saturation. To approximate the unknown nonlinear functions and overcome the problem of explosion of complexity, a Fuzzy logic system is combined with the dynamic surface control in the backstepping design technique. To ensure the output constraints satisfaction, an asymmetric time-varying Barrier Lyapunov Function (BLF) is used. Moreover, by applying the minimal learning parameter technique, the number of the online parameters update for each subsystem is reduced to 2. Hence, the semi-globally uniformly ultimately boundedness (SGUUB) of all the closed-loop signals with appropriate tracking error convergence is guaranteed. The effectiveness of the proposed control is demonstrated by two simulation examples.

Fault detection in non‐linear system has drawn a lot of attention recently. A typical solution is the generalization of linear methods to include non‐linear dynamics. This study addresses fault detection in non‐linear systems by extending parity relations using Takagi‐Sugeno (T.S) fuzzy models. Parity equations for linear systems are a residual generation method that has appealing capabilities in fault detection. T.S fuzzy systems are also extensively used in modelling of non‐linear systems. In this paper, parity equations are rewritten in the form of non‐linear systems that can be modelled by T.S fuzzy system. An advantage of this approach is that parity vector can be derived from relations explicitly. An algorithm is proposed to show how a residual can be generated in this manner. Simulation results on the fault detection of a mass‐spring‐damper system show the effectiveness of the proposed method.

In this paper, a recurrent neural network coupled with Kalman filter is proposed to identify dynamic terms of robotic manipulator. By cooperating some inherent characteristics of robot, this network has the capability to individually identify nonlinear terms using Weighted Augmentation Error (WAE). To present the infrastructure of architecture, an adaptive scheme based on the conventional Back Propagation (BP) is firstly driven using the Gradient Descent (GD) method. Additionally, a stable adaptive updating rule is extracted from the discrete time Lyapunov candidate as an approach for the general nonlinear system identification. Then, this approach is applied to the predefined network. To experimentally validate the computational efficiency and control applicability of the proposed method, Adaptive Neural Network Based Inverse Dynamic Control (ANN-Based-IDC) is employed on a laboratory-scaled twin-rotor CE-150 helicopter. This experiment illustrates enhancement of steady-state performance from 2-to-3 times more in compared with simple PID. Moreover, disturbance rejection and robustness tests admit capability of the method for online dynamic identification in the presence of output and dynamic perturbation.

In this letter, we point out that the asymptotic convergence, claimed in Theorem 2, of the output residual and parameter estimation error after fault occurrence are guaranteed by the performance of the fault diagnosis observer is not quite right. The proof of the asymptotic convergence is contributed by Lemma 1 and negative semidefiniteness of the first difference of Lyapunov candidate function. Here, it is shown that utilizing Lemma 1 yields in some disputed points in the proof of Theorem 2. On the other hand, the proof of Theorem 2 is not mathematically correct. Therefore, the guarantee of the asymptotic convergence mentioned for FD observer in Theorem 2 is not realizable.

A spatially-constrained clustering algorithm is presented in this paper. This algorithm is a distributed clustering approach to fine-tune the optimal distances between agents of the system to strengthen the data passing among them using a set of spatial constraints. In fact, this method will increase interconnectivity among agents and clusters, leading to improvement of the overall communicative functionality of the multi-robot system. This strategy will lead to the establishment of loosely-coupled connections among the clusters. These implicit interconnections will mobilize the clusters to receive and transmit information within the multi-agent system. In other words, this algorithm classifies each agent into the clusters with the lowest cost of local communication with its peers. This research demonstrates that the presented decentralized method will actually boost the communicative agility of the swarm by probabilistic proof of the acquired optimality. Hence, the common assumption regarding the full-knowledge of the agents’ primary locations has been fully relaxed compared to former methods. Consequently, the algorithm’s reliability and efficiency is confirmed. Furthermore, the method’s efficacy in passing information will improve the functionality of higher-level swarm operations, such as task assignment and swarm flocking. Analytical investigations and simulated accomplishments, corresponding to highly-populated swarms, prove the claimed efficiency and coherence.

In this study, a novel fuzzy unknown input observer for robust fault estimation scheme is developed when both faults and unknown input are considered. The proposed scheme includes component fault with nonlinear distribution matrix in state equation, unknown input signal in state and output equations. After that, Takagi-Sugeno (T-S) model is used to create multiple models. While T-S model is used for only the nonlinear distribution matrix of the fault signal, a larger category of nonlinear system will be included. Two set of observers are considered, the first one is extended fuzzy unknown input observer (EFUIO) and the other one is fuzzy sliding mode observer (FSMO). The approach decoupled the faulty subsystem from the rest of the system through a series of linear transformations. Then, the objective is to design EFUIO to guarantee the asymptotic stability of the error dynamic using the Lyapunov method. Unknown input is removed; meanwhile, FSMO is designed for faulty subsystem to guarantee estimation of fault. Sufficient conditions are established in order to guarantee the convergence of the state estimation error and the results are formulated in the form of linear matrix inequalities (LMIs). Finally, a simulation study on an electromagnetic suspension system (EMS) is presented to demonstrate the performance of the results compared with a pure SMO.

Filtering is an effective method of alarm management family that can reduce false and missed alarm rates significantly. Simple and effective techniques of fault diagnosis methods are popular in industry. So, deriving a simple analytic filter design approach is important. This study proposes a simple analytic linear filter design based on a probabilistic model of the system. At last, the effectiveness of the proposed method is showed in the deposition fault detection of a V94. 2 gas turbine with 162.1 MW and 50 Hz as the nominal power and frequency respectively. It is built by MAPNA group (originally built by SIEMENS) and set up in Shiraz power plant, Shiraz city of Iran.

This paper presents a Gaussian radial basis function neural network based on sliding mode control for trajectory tracking and vibration control of a flexible joint manipulator. To study the effectiveness of the controllers, designed controller is developed for tip angular position control of a flexible joint manipulator. The adaptation laws of designed controller are obtained based on sliding mode control methodology without calculating the Jacobian of the flexible joint system. Also in this study, the anti-control is applied to reduce the deflection angle of flexible joint system. To achieve this goal, the chaos dynamic must be created in the flexible joint system. So, the flexible joint system has been synchronized to chaotic gyroscope system. In this study, control and anti-control concepts are applied to achieve the high quality performance of flexible joint system. It is tried to design a controller which is capable to satisfy the control and anti- control aims. The performances of the proposed control are examined in terms of input tracking capability, level of vibration reduction and time response specifications. Finally, the efficacy of the proposed method is validated through experimentation on QUANSER’s flexible-joint manipulator.

In this contribution, full probability distribution of parameters of ARX model is obtained for on-line problems by means of Bayesian approach and Markov chain Monte Carlo method (MCMC), which provides the ability to be applied on time-varying ARX models as well. Full probability distribution of parameters represent whole available knowledge of parameters. So, decision makers can follow any policies to make decision about point estimation, like dynamic point estimation. Moreover, the Bayesian approach has great potential in combining sources of knowledge much more easier. To decrease the computational efforts, full probability of model parameters are updated based on size-varying partitions. Furthermore, incorporating the posterior probability of previous partition into the jump probability of current partition, in MCMC method, improves the performance of the proposed algorithm from the computation and convergence rate point of view. Simulation results demonstrate the effectiveness and validity of the proposed algorithm.

In this paper, a novel scheme is presented to conquer the motion-planning problem for autonomous space robots. Minimizing the consumed energy of atomic batteries within the daily planetary missions of robot on the planet is taken into account, i.e., utilization of the generated solar power by its embedded photocells leads to saving energy of batteries for night missions. Aforementioned objective could be acquired by appropriate interaction of motion planning paradigm with shadows of obstacles. Modeling of the shadow with the proposed artificial potential field leads to generalize the concept of potential fields not only for static and dynamic obstacles but also for being confronted with the intrinsic time-variant phenomena such as shadows. With due attention to the noticeable computational complexity of the introduced strategy, fuzzy techniques are applied to optimize the sampling times effectively. To accomplish this objective, a smart control scheme based on the fuzzy logic is mounted to the primitive version of algorithm. Regarding the need to identify some structural parameters of obstacles, PIONEER™ mobile robot is designed as a test bed for the verification of simulated results. Investigation on empirical accomplishments shows that the goal-oriented definition of Time–Variant Artificial Potential Fields is able to resolve the motion-planning problem in planetary applications.

In this paper, a novel adaptive hierarchical fuzzy control system based on the variable structure control is developed for a class of SISO canonical nonlinear systems in the presence of bounded disturbances. It is assumed that nonlinear functions of the systems be completely unknown. Switching surfaces are incorporated into the hierarchical fuzzy control scheme to ensure the system stability. A fuzzy soft switching system decides the operation area of the hierarchical fuzzy control and variable structure control systems. All the nonlinearly appeared parameters of conclusion parts of fuzzy blocks located in different layers of the hierarchical fuzzy control system are adjusted through adaptation laws deduced from the defined Lyapunov function. The proposed hierarchical fuzzy control system reduces the number of rules and consequently the number of tunable parameters with respect to the ordinary fuzzy control system. Global boundedness of the overall adaptive system and the desired precision are achieved using the proposed adaptive control system. In this study, an adaptive hierarchical fuzzy system is used for two objectives; it can be as a function approximator or a control system based on an intelligent-classic approach. Three theorems are proven to investigate the stability of the nonlinear dynamic systems. The important point about the proposed theorems is that they can be applied not only to hierarchical fuzzy controllers with different structures of hierarchical fuzzy controller, but also to ordinary fuzzy controllers. Therefore, the proposed algorithm is more general. To show the effectiveness of the proposed method four systems (two mechanical, one mathematical and one chaotic) are considered in simulations. Simulation results demonstrate the validity, efficiency and feasibility of the proposed approach to control of nonlinear dynamic systems.

The growing availability of high-resolution satellite imagery provides an opportunity for identifying road objects. Most studies associated with road detection are scene-related and also based on the digital number of each pixel. Because images can provide more details (including color, size, shape, and texture), object-based processing is more advantageous. Therefore, in this paper, to handle the existing uncertainty of satellite image pixel values, using type-2 fuzzy set theory in combination with object-based image analysis is proposed. Because the main challenges of the type-2 fuzzy set are parameter tuning and extensive computations, a hybrid genetic algorithm (GA) consisting of Pittsburgh and cooperative-competitive learning schemes is proposed to address these problems. The most prominent feature of our research in this work is to establish a comprehensive object-based type-2 fuzzy logic system that enables us to detect roads in high-resolution satellite images with no training data. The validation assessment of road detection results using the proposed framework for independent images demonstrates the capability and efficiency of our method in identifying road objects. For more evaluation, a type-1 fuzzy logic system with the same structure as type-2 is tuned. Evaluations show that type-1 fuzzy logic system quality in training is very similar to that of the proposed type-2 fuzzy framework. However, in general, its lower accuracy, as inferred by validation assessments, makes the type-1 fuzzy logic system significantly different from the proposed type-2.

In this study, seismic attributes have been used to estimate well logs in one of the Iranian petroleum reservoirs. Three static methods have been evaluated: the linear model, the multilayer perceptron (MLP) and the radial basis function (RBF). For linear case, the selection of appropriate attributes was determined by forward selection and for nonlinear one, the selection was based on the genetic algorithm (GA) result. Parameters of nonlinear models were determined by cross-validation and then well logs were estimated. By comparing estimated and actual logs, RBF has the best performance with least training error. Since well logs contain high frequency content, so localized networks such as RBF has better performance than MLP through the study data set.

This paper presents a variable structure rule-based fuzzy control for trajectory tracking and vibration control of a flexible joint manipulator by using chaotic anti-control. Based on Lyapunov stability theory for variable structure control and fuzzy rules, the nonlinear controller and some generic sufficient conditions for global asymptotic control are attained. The fuzzy rules are directly constructed subject to a Lyapunov function obtained from variable structure surfaces such that the error dynamics of control problem satisfy stability in the Lyapunov sense. Also in this study, the anti-control is applied to reduce the deflection angle of flexible joint system. To achieve this goal, the chaos dynamic must be created in the flexible joint system. So, the flexible joint system has been synchronized to chaotic gyroscope system. In this study, control and anti-control concepts are applied to achieve the high quality performance of flexible joint system. It is tried to design a controller which is capable to satisfy the control and anticontrol aims. The performances of the proposed control are examined in terms of input tracking capability, level of vibration reduction and time response specifications. Finally, the efficacy of the proposed method is validated through experimentation on QUANSER’s flexible-joint manipulator.

Electro-hydrostatic actuator (EHA) is a kind of hydraulic system in which fluid is routed directly by pump to the actuator. In this study, a novel adaptive fuzzy-PID controller is developed to improve position controlling performance of an EHA. First of all, design and simulation of an EHA by using multidisciplinary modelling method is presented. This model is evaluated by soft validation method. The whole proposed novel control system is composed of a pair of interconnected subsystems, that is, a simple fuzzy-PID controller (SFPID) and a radial basis function neural network (RBFNN) to enhance the tracking performance. The RBFNN fuzzy-PID control (RBFNNF-PID) is applied to EHA. Also, SFPID control, fuzzy-PID control based on extended Kalman filter using grey predictor (FPIDKG) and simple adaptive control (SAC) as significant controls are applied to EHA. The simulation results have shown a significant improvement in transient response and reduction in sum square error (SSE).

Prediction of seasonal influenza epidemics is certainly a forming and effective step towards taking appropriate preventive actions. Improvement on public informing, decreasing the number of infected cases, undesirable effects and deaths due to influenza and also increasing vigilance of Iranian Influenza Surveillance System (IISS), have been practical goals of this research. A forecasting system has been designed and developed using Artificial Neural Networks (ANNs). It is a novel research as a novel dataset has been exploited. The data are categorized in two groups of climatic parameters (temperature, humidity, precipitation, wind speed & sea level pressure) and number of patients (number of total referrals and number of patients with Influenza-Like Illnesses (ILI)). In order to evaluate the model performance, different cost functions are defined and results indicate that the model provides the possibility of a satisfactory forecasting and is practically helpful to achieve the objectives already claimed.

Modern systems are required to guarantee a high degree of safety and self-diagnostics capabilities. This paper investigates the problem of state fault diagnosis in nonlinear systems with modeling uncertainties. In contrast with common literature, the fault diagnosis scheme is proposed in discrete time domain. This property relaxes the risk of stability and performance degradation in deriving discrete equivalent of continuous methods. An estimator is designed in order to generate residual signal by utilizing a proper nonlinear state transformation. A robust compensator term is implemented in estimator to decrease effect of modeling uncertainties and approximation error on residual signal. When the residual signal is exceeded detection threshold, an on-line fault approximator is turned on and trained by appropriate parameter update law. An extra term is considered in update rule to overcome the need of persistency of excitation (PE). The implement of all robust compensator term, PE relaxing term and proper parameter adaption law improve the accuracy of fault reconstruction. The result would be obviously vital in tolerant and time-life prediction stages after fault diagnosis.

In this study, fault tolerant control for a Rotary Inverted Pendulum (RIP) has been improved by using chaos synchronization with adding a chaotic signal as a reference. Rotary inverted pendulum is a nonlinear, under-actuated, unstable and non-minimum-phase system. The proposed control consists of a state-feedback (LQR) and a fuzzy-PID control. The state- feedback control is used to stabilize system near the operating point, and the fuzzy-PID is used to track the chaos signal. PID controller gains adjust by fuzzy rule. The designed controller is implemented on a Quanser laboratory system.

In this study, first by using the collected real data from a 10000 cubic-meter Qazvin-kowsar water supply reservoir is modeled by nonlinear output error (NOE) structure, then a neural nonlinear controller based on the MLP neural network according to created model is designed in order to control the tank water level. The operation of the proposed controller is compared by a PID controller which its coefficients is optimized by genetic algorithm. Results of the simulation indicates that the neural nonlinear controller has a better function than the PID controller, and also this controller is able to control the level water of the tank appropriately regardless the consumer profile in all conditions even in consumer picks.

In this study it is attempted to describe the structure and procedure of training for the Interval Type-2 Fuzzy Logic inference System completely. To achieve this goal Adaptive Network- based Fuzzy Inference System (ANFIS) structure has been generalized to interval type-2 fuzzy, also all of the relations to describe inference structure and all of the necessary differentiation to adjust parameters with Gradient descent and Levenberg-Marquardt method has been brought. Described structure has been used to forecast Mackey-Glass chaotic time- series that polluted with additive uncertain domain noise. Using mentioned procedure for parameters adjustment achieved acceptable results.

Neural network based controller is used for controlling a mobile robot system. Feedback error learning (FEL) can be regarded as a hybrid control to guarantee stability of control approach. This paper presents simulation of a mobile robot system controlled by a FEL neural network and PD controllers. This feedback error-learning controller benefits from both classic and adaptive controller properties. The simulation results demonstrate that this method is more feasible and effective for mobile robot system control.

In this paper, modeling, identification and control of a real 162MW heavy duty industrial gas turbine is taken into account. This work is aimed to introduce a simple and comprehensive model to test various controllers. Rowen's model is used to present the mechanical behavior of the gas turbine, while the identification of it is done using a feedforward neural network. The control rules of the turbine are applied on both models and a comparison of the results is also presented.

This paper deals with the issue of position control of an Electro-Hydrostatic Actuator (EHA) using an adaptive PID controller based on neurofuzzy network. In this relation, the design and simulation of an electro-hydrostatic actuation system referred to as EHA using multidisciplinary modeling method is presented. In recent years, fuzzy-PID controller is one of the main controllers that apply to the EHA systems. To improve the response of this controller, another control technique is needed to combine with the fuzzy-PID, and also, training some parameters of fuzzy-PID technique is a solution. The whole of new controller is composed of pair of interconnected subsystems, that is, an RBF network and conventional fuzzy-PID controller to enhance the tracking performance. Results show a significant improvement in transient response is achieved in comparison with a conventional fuzzy-PID control.

In this study, a new adaptive controller is proposed for position control of pneumatic systems. Difficulties associated with the mathematical model of the system in addition to the instability caused by Pulse Width Modulation (PWM) in the learning-based controllers using gradient descent, motivate the development of a new approach for PWM pneumatics. In this study, two modified Feedback Error Learning (FEL) methods are suggested and the their effectiveness are validated by experimental tracking data. The first one is a combination of PD (Proportionalâ Derivative) and RBF (Radial Basis Function) and in the second one RBF is replaced by ANFIS (Adaptive Neuro-Fuzzy Inference System). The robustness to varying mass is also examined. The experimental results show that the proposed algorithms, especially with ANFIS, are able to give good performance regardless of any uncertainties.

This study applies technique PDC (parallel distributed compensation) for speed control of a Digital Servo System. PDC method is based on nonlinear Takagi-Sugeno (TS) fuzzy model. Also in this study Neural Adaptive is used for velocity control and identification of a Digital Servo System. It is shown that these techniques can be successfully used to stabilize any chosen operating point of the system. All derived results are validated by experimental and computer simulation. The controllers which introduced have big range for control the system. We compare PDC controller with Neural Adaptive controller results and PID controller.

This brief proposes modified projective synchronization (MPS) methods for underactuated unknown heavy symmetric chaotic gyroscope systems via optimal Gaussian radial basis adaptive variable structure control. Chaotic gyroscope systems are considered as underactuated systems where a control input is designed to synchronize the two degree of freedoms interactions. Until now, no investigation of this subject with one control input has been presented. The importance of obtaining synchronization objectives is specified when the dynamics of gyroscope system are unknown. In this brief, using the neural variable structure control technique, a control law is established that guarantees the MPS of underactuated unknown chaotic gyros. In the neural variable structure control, Gaussian radial basis functions are utilized to estimate online the system dynamic functions. Adaptation laws of the online estimator are derived in the sense of the Lyapunov function. Moreover, online and offline optimizers are applied to optimize the energy of the control signal. The proposed solution is generalized to chaos control of the mentioned gyroscopes. Numerical simulations are presented to verify the proposed synchronization methods.

Type-1 fuzzy sets cannot fully handle the uncertainties. To overcome the problem, type-2 fuzzy sets have been proposed. The novelty of this paper is using interval type-2 fuzzy logic controller (IT2FLC) to control a flexible-joint robot with voltage control strategy. In order to take into account the whole robotic system including the dynamics of actuators and the robot manipulator, the voltages of motors are used as inputs of the system. To highlight the capabilities of the control system, a flexible joint robot which is highly nonlinear, heavily coupled and uncertain is used. In addition, to improve the control performance, the parameters of the primary membership functions of IT2FLC are optimized using particle swarm optimization (PSO). A comparative study between the proposed IT2FLC and type-1 fuzzy logic controller (T1FLC) is presented to better assess their respective performance in presence of external disturbance and unmodelled dynamics. Stability analysis is presented and the effectiveness of the proposed control approach is demonstrated by simulations using a two-link flexible-joint robot driven by permanent magnet direct current motors. Simulation results show the superiority of the IT2FLC over the T1FLC in terms of accuracy, robustness and interpretability.

Despite active research and significant progress in the last three decades on control of human eye movements, it remains challenging issue due to its applications in prosthetic eyes and robotics. Till now, no considerable investigation of this subject is presented in the interdisciplinary sciences. The goal of this paper is to present a distinguished survey of existing literature on the intelligent control of the human eye movements system applied in a huggable pet-type robot as a biomechatronic system. In this study, the basic knowledge of human eye movements control is explained to show how the neural networks in the brainstem control the human eye movements. The geometry and model of human eye movements system are investigated and this system is considered as a nonlinear control system. The specified model may only be an academic exercise. It can have scientific importance in understanding of the human movement system in general. Also, it can be useful for robotics. Intelligent methods such as artificial neural networks and fuzzy neural networks are proposed to control the human eye movements and numerical simulations are presented. It is discussed that the intelligent controls applied to control of human eye movements system are emulated from the neural controls in biological system.

Breast cancer is the cause of the most common cancer death in women. Early detection of the breast cancer is an effective method to reduce mortality. Fuzzy Neural Networks (FNN) comprises an integration of the merits of neural and fuzzy approaches, enabling one to build more intelligent decision-making systems. But increasing the number of inputs causes exponential growth in the number of parameters in Fuzzy Neural Networks (FNN) and computational complexity increases accordingly. This phenomenon is named as “curse of dimensionality”. The Hierarchical Fuzzy Neural Network (HFNN) and the Fuzzy Gaussian Potential Neural Network (FGPNN) are utilized to deal this problem. In this study, the HFNN and FGPNN by using new training algorithm, are applied to the Wisconsin Breast Cancer Database to classify breast cancer into two groups; benign and malignant lesions. The HFNN consists of hierarchically connected low-dimensional fuzzy neural networks. It can use fewer rules and parameters to model nonlinear system. Moreover, the FGPNN consists of Gaussian Potential Function (GPF) used in the antecedent as the membership function. When the number of inputs increases in FGPNN, the number of fuzzy rules does not increase. The performance of HFNN and FGPNN are evaluated and compared with FNN. Simulation results show the effectiveness of these methods even with less rules and parameters in performance result. These methods maintain the accuracy of original fuzzy neural system and have high interpretability by human in diagnosis of breast cancer.

This paper proposes the chaos control and the modified projective synchronization methods for unknown heavy symmetric chaotic gyroscope systems via Gaussian radial basis adaptive backstepping control. Because of the nonlinear terms of the gyroscope system, the system exhibits chaotic motions. Occasionally, the extreme sensitivity to initial states in a system operating in chaotic mode can be very destructive to the system because of unpredictable behavior. In order to improve the performance of a dynamic system or avoid the chaotic phenomena, it is necessary to control a chaotic system with a regular or periodic motion beneficial for working with a particular condition. As chaotic signals are usually broadband and noise-like, synchronized chaotic systems can be used as cipher generators for secure communication. Obviously, the importance of obtaining these objectives is specified when the dynamics of gyroscope system are unknown. In this paper, using the neural backstepping control technique, control laws are established which guarantees the chaos control and the modified projective synchronization of unknown chaotic gyroscope system. In the neural backstepping control, Gaussian radial basis functions are utilized to on-line estimate the system dynamic functions. Also, the adaptation laws of the on-line estimators are derived in the sense of Lyapunov function. Thus, the unknown chaotic gyroscope system can be guaranteed to be asymptotically stable. Also, the control objectives have been achieved. The proposed method allows us to arbitrarily adjust the desired scaling by controlling the slave system. It is not necessary to calculate the Lyapunov exponents and the eigenvalues of the Jacobian matrix, which makes it simple and convenient. Also, it is a systematic procedure for modified projective synchronization of chaotic systems and it can be applied to a variety of chaotic systems no matter whether it contains external excitation or not. Notice that it needs only one controller to realize modified projective synchronization no matter how much dimensions the chaotic system contains and the controller is easy to be implemented. It seems that the proposed method can be useful for practical applications of chaotic gyroscope systems in the future. Numerical simulations are presented to verify the proposed control and synchronization methods.

In a cement factory, a rotary kiln is the most complex component and it plays a key role in the quality and quantity of the final product. This system involves complex nonlinear dynamic equations that have not been completely worked out yet. In conventional modeling procedures, a large number of the involved parameters are crossed out and an approximation model is presented instead. Therefore, the performance of the obtained model is very important and an inaccurate model may cause many problems in the design of a controller. This study presents a Takagi-Sugeno (TS)-type fuzzy system called a wavelet projection fuzzy inference system (WPFIS) in which a dimension reduction section is used at the input stage of the fuzzy system. In order to clarify the structure of the extracted features, structural learning with forgetting (SLF) based on Minkowski norms is proposed. In addition, gradient descent (GD) was used as a training algorithm. The results show that the proposed method has higher performance in comparison with conventional models. The data collected from Saveh White Cement Company were used in our simulations.

In this paper Fault Detection and Isolation (FDI) is shown as a pattern classification problem which can be solved using clustering techniques. Gath-Geva clustering (GGC) is exploited as optimal form by a performance assessment rule for fault detection, while multistage Gath-Geva clustering is employed for the intent of fault isolation. Furthermore since Visbreaker unit is a large scale process, a novel hybrid method on the basis of Principle Component Analysis and Genetic Algorithm optimization was also proposed in order to cope with the curse of dimensionality and complexity of computation problems. There are two main percentile criteria for validation of fault detection namely specificity and sensitivity. Evaluation of fault isolation has been depicted in confusion matrix. For analysis and visualization of the correlated high dimensional data, PCA maps the data point into lower dimensional space. The proposed FDI approaches have been evaluated through experimental Visbreaker process unit data collected in oil refinery.

Development of a fault detection scheme for nonlinear systems is often difficult due to complexity of the system. In this study a new method, based on parity relations for linear systems, is proposed to detect faults in nonlinear systems that can be modeled by Takagi- Sugeno (TS) fuzzy system. This method is an intuitive generalization of parity relations, because TS fuzzy system uses local linear models. Results of simulation and implementation on a rotary inverted pendulum show that faults can be detected very well.

In this paper, fault tolerant synchronization of chaotic gyroscope systems versus external disturbances via Lyapunov rule-based fuzzy control is investigated. Taking the general nature of faults in the slave system into account, a new synchronization scheme, namely, fault tolerant synchronization, is proposed, by which the synchronization can be achieved no matter whether the faults and disturbances occur or not. By making use of a slave observer and a Lyapunov rule-based fuzzy control, fault tolerant synchronization can be achieved. Two techniques are considered as control methods: classic Lyapunov-based control and Lyapunov rule-based fuzzy control. On the basis of Lyapunov stability theory and fuzzy rules, the nonlinear controller and some generic sufficient conditions for global asymptotic synchronization are obtained. The fuzzy rules are directly constructed subject to a common Lyapunov function such that the error dynamics of two identical chaotic motions of symmetric gyros satisfy stability in the Lyapunov sense. Two proposed methods are compared. The Lyapunov rule-based fuzzy control can compensate for the actuator faults and disturbances occurring in the slave system. Numerical simulation results demonstrate the validity and feasibility of the proposed method for fault tolerant synchronization.

Differential pipe sticking (DPS) is one of the most conventional and serious problems in drilling operations that imposes some extra costs to companies. This phenomenon originates mainly from improper mud properties, bottomhole assembly (BHA) (contacting area), still pipe time, and differential pressure between the formation and the drilling mud. Investigation on various conditions that lead to DPS makes it possible to develop some preventive treatments to avoid this problem's occurrence. In the past, statistical methods were applied in this area, but recently artificial neural network (ANN) approaches are frequently being used. ANNs have some priorities over conventional statistical methods such as the model-free form of predictions, tolerance to data errors, data-driven nature, and fast computation. On the other hand, the designed ANNs have some shortcomings and restrictions as they are developed to predict problems. In this paper, to solve most of the existing disadvantages of ANNs, a novel support-vector machine (SVM) approach has been developed to predict a DPS occurrence in horizontal and sidetracked wells in Iranian offshore oil fields. The results from the analysis have shown the potential of the SVM and ANNs to predict DPS, with the SVM results being more promising.

This paper presents a nonlinear control for trajectory tracking and vibration control of a flexible joint manipulator by using chaotic gyroscope synchronization. To study the effectiveness of the controllers, designed controller is developed for tip angular position control of a flexible joint manipulator. Based on Lyapunov stability theory, the nonlinear controller and some generic sufficient conditions for global asymptotic control are attained. In this study, the anti-control is applied to reduce the deflection angle of flexible joint system. To achieve this goal, the chaos dynamic must be created in the flexible joint system. So, the flexible joint system has been synchronized to chaotic gyroscope system. In this study, control and anti-control concepts are applied to achieve the high quality performance of flexible joint system. It is trying to design a controller which is capable to satisfy the control and anti-control aims. The performances of the proposed control are examined in terms of input tracking capability and level of vibration reduction. Finally, the efficacy of the proposed method is validated through experimentation on QUANSER’s flexible joint manipulator.

This paper proposes the modified projective synchronization for heavy symmetric dissipative gyroscope systems via backstepping control. Because of the nonlinear terms of the gyroscope system, the system exhibits complex and chaotic motions. Using the backstepping control technique, control laws are established which guarantees the hybrid projective synchronization including synchronization, anti-synchronization and projective synchronization. By Lyapunov stability theory, control laws are proposed to ensure the stability of the controlled closed-loop. Numerical simulations are presented to verify the proposed synchronization approach. This paper demonstrates that synchronization and anti- synchronization can coexist in dissipative gyroscope systems via nonlinear control.

Overhead crane is an industrial structure that used widely in many harbors and factories. It is usually operated manually or by some conventional control methods. In this paper, we propose a hybrid controller includes both position regulation and anti-swing control. According to Takagi-Sugeno fuzzy model of an overhead crane and genetic algorithm, a fuzzy controller is designed with parallel distributed compensation and Linear Quadratic Regulation. Using genetic algorithm, important fuzzy rules are selected and so the number of rules decreased and design procedure need less computation and its computation needs less time. Further, the stability of the overhead crane with the parallel distributed fuzzy LQR controller is discussed. The stability analysis and control design problems is reduced to linear matrix inequality (LMI) problems. Simulation results illustrated the validity of the proposed parallel distributed fuzzy LQR control method and it was compared with a similar method parallel distributed fuzzy controller with same fuzzy rule set.

In the petroleum industry perforating is a method of making holes through the casing opposite the production formation to allow the oil or gas to flow into the well. In the current explosive shaped charge perforation method there arc some serious problems, such as producing debris. uncontrollable hole size and shape, compaction of rock formation in the area next to the tunnel and decreasing permeability. Recent advances in high power laser technology provide a new alternative to replace the current perforating gun. Due to the nature of oil and gas reservoirs, one of the challenges in laser perforation is the laser beam-fluid interaction that results in laser power loss (LPL), In this paper, feed-forward network with back-propagation and generalized regression neural networks have been developed to predict LPL in the laser beam-fluid interaction during laser perforation. Effective parameters in the laser-fluid interaction such as laser power, fluid viscosity and fluid thickness which arc related to laboratory tests done by ytterbium-doped multi-clad fibre laser are the inputs and LPL is the output of the neural networks. The developed neural networks have shown high correlation coefficients with low error and the LPL for the laser beam-fluid interaction during laser perforation was predicted with high accuracy.

Nowadays computer games have become a billion dollar industry. One of the important factors in success of a game is its similarity to the real world. As a result, many AI approaches have been exploited to make game characters more believable and natural. One of these approaches which has received great attention is Fuzzy Logic. In this paper a Fuzzy Rule-Based System is employed in a fighting game to reach higher levels of realism. Furthermore, behavior of two fighter bots, one based on the proposed Fuzzy logic and the other one based on a scripted AI, have been compared. It is observed that the results of the proposed method have less behavioral repetition than the scripted AI, which boosts human players' enjoyment during the game.

This study presents a novel controller of magnetic levitation system by using new neuro-fuzzy structures which called flexible neuro-fuzzy systems. In this type of controller we use sliding mode control with neuro-fuzzy to eliminate the Jacobian of plant. At first, we control magnetic levitation system with Mamdanitype neuro-fuzzy systems and logical-type neuro-fuzzy systems separately and then we use two types of flexible neuro-fuzzy systems as controllers. Basic flexible OR-type neuro-fuzzy inference system and basic compromise AND-type neuro-fuzzy inference system are two new flexible neuro-fuzzy controllers which structure of fuzzy inference system (Mamdani or logical) is determined in the learning process. We can investigate with these two types of controllers which of the Mamdani or logical type systems has better performance for control of this plant. Finally we compare performance of these controllers with sliding mode controller and RBF sliding mode controller.

In this paper, fault tolerant synchronization of chaotic gyroscope systems via Gaussian RBF neural network based on sliding mode control is investigated. Taking a general nature of fault in the slave system into account, a new synchronization scheme, namely, fault-tolerant synchronization, is proposed, by which the synchronization can be achieved no matter if the fault and disturbance occur or not. By making use of a slave-observer and Gaussian RBF Neural Network Based on Sliding Mode Control, the fault tolerant synchronization can be achieved. The adaptation law of designed controller is obtained based on sliding mode control methodology without calculating the Jacobian of the system. The proposed method can compensate the actuator faults and disturbances occurred in the slave system. Numerical simulation results demonstrate the validity and feasibility of the proposed method to fault tolerant synchronization.

This paper aims to increase the classification specificity by using multi classifier system. First, a novel pixel search approach is applied to find significant region in images. Fuzzy C-means is utilized to determine the clear boundary of tumor. Then, shape and texture features are extracted from region of interest. Genetic algorithm is applied to select the best feature used for classifiers. Several neural networks and support vector machine are considered as classifiers that classify the data into benign and malignant group. To improve the performance of classification, three classifiers that have the best results among all applied methods are combined together that they have been named as multi classifier system. For each lesion, final detection as malignant or benign has been evaluated, when the same results are achieved from two classifiers of multi classifier system. Notice that the Jack-Knife technique is applied in this study, because it is useful for small data base as ours gotten from Milad Hospital in Tehran, Iran.

This paper proposes the generalized projective synchronization (GPS) of uncertain chaotic systems with external disturbance via Gaussian radial basis adaptive sliding mode control (GRBASMC). A sliding surface is adopted to ensure the stability of the error dynamics in sliding mode control. In the neural sliding mode controller, a Gaussian radial basis function is utilized to online estimate the system dynamic function. The adaptation law of the control system is derived in the sense of Lyapunov function, thus the system can be guaranteed to be asymptotically stable. The proposed method allows us to arbitrarily adjust the desired scaling by controlling the slave system. It is not necessary to calculate the Lyapunov exponents and the eigen values of the Jacobian matrix, which makes it simple and convenient. Also, it is a systematic procedure for GPS of chaotic systems and it can be applied to a variety of chaotic systems no matter whether it contains external excitation or not. Note that it needs only one controller to realize GPS no matter how much dimensions the chaotic system contains and the controller is easy to be implemented. The proposed method is applied to three chaotic systems: Genesio system, Lur’e like system and Duffing system.

In this paper, a new algorithm is presented in using Multi Layer Perceptron (MLP) and Radial Base Function (RBF) to predict Ischemia diseases by Electrocardiogram (ECG) signals. The process would be very difficult due to non-stationary and nonlinear characteristics of ECG signals. MLP and RBF algorithms are well known in predicting the problems. However, they have not been used for real time prediction through signals, especially bio signals such as ECG. Pre-processing is necessary for ECG signal in order to detect QRS complex. Regarding the extract influential features in Ischemia disease, the baseline wandering and noise suppression are done. MLP and RBF, the predictors, are employed to foresee the further next beats in ECG signals. The validity of predictor accuracy is evaluated by Root Mean Square Error (RMSE) criterion. After the prediction stage, The predicted beats are classified by Adaptive Neuro-Fuzzy network (ANFIS) classifier as ischemic and normal. MLP and RBF are tested for their abilities in order to predict Ischemic Heart Disease (IHD) upon ECG signals. The performances of classified beats are evaluated based on computed Sensitivity (Se) and Specificity (Sp). In this study several ECG signals recorded by European Society of Cardiology for ST-T database are used. By applying prediction methods (Direct and Recursive Predictions) 48 steps can be predicted ahead in ECG signal. Then the predicted beats are classified as Ischemic or normal beats. Therefore, the ischemic beats can be predicted in 48 steps ahead. By comparing the results obtained in this study, the MLP and RBF networks are evaluated for their capabilities in predicting Ischemia. According to this comparison, MLP shows better results and the results of ANFIS as a classifier has been satisfactory enough in classification of Ischemic beats. Therefore, these results can be used for early diagnosis of Ischemic Heart Disease (IHD).

This paper presents a Neuro-fuzzy based method using local linear model trees (LOLIMOT) train algorithm for nonlinear identification of a catalytic reformer unit in oil refinery plant. This unit include highly nonlinear behaviour and it is complicated to obtain an accurate physical model. There for, it is necessary to use such appropriate method providing suitable while preventing computational complexities. LOLIMOT algorithm as an incremental learning algorithm has been used several time as a well-known method for nonlinear system identification and estimation. For comparison, Multi Layer Perceptron (MLP) and Radial Bases Function (RBF) neural networks as well-known methods for nonlinear system identification and estimation are used to evaluate the performance of LOLIMOT. The results presented in this paper clearly demonstrate that the LOLIMOT is superior to other methods in identification of nonlinear system such as catalytic reformer unit (CRU).

This study addresses a nonlinear trajectory tracking control problem for a kinematics Model of nonholonomic mobile robot with considering next 2 time path curvature. The tracking control of mobile robot using two cascade controllers is presented. The first fuzzy controller produces a variable which shows curvature of the path and is considered as one of the inputs of the second fuzzy controller. Adaptive Neuro Fuzzy Inference System (ANFIS) is applied as second stage controller for the solution of the path tracking problem of mobile robots. The inputs value to fuzzy logic layer are VC, C, dR & dθ the robot current linear velocity, trajectory curvature, distance from the robot actual position to the next desired position, and difference between the angles of the dθ and the robot actual heading, respectively. A gradient descent learning algorithm is used to adjust the parameters. That present controller is compared with previous work to confirm its effectiveness.

The use of intravenous drugs in general anesthesia is increasingly popular. Because of relatively precise injection rate, the goal of consistent control is possible. Because of using different drugs in full anesthesia for adequate hypnosis, analgesia and muscle relaxation, the interaction between drugs is more considerable especially when intravenous drugs are used. In this paper we use a developed Pharmacokinetic-Pharmacodynamic model which considers the interaction between two more popular intravenous drugs, Propofol for hypnosis and Remifentanil as an analgesic drug, to design a closed-loop system. The Radial Basis Function (RBF) controller as an adaptive neural controller was designed and adaptive properties of this structure in confront of variations in model parameters values was investigated. Trying to improve the tracking performance, one of most popular methods in hybrid control, Feedback Error Learning (FEL), was utilized.

This paper presents a new approach for breast cancer detection based on Hierarchical Fuzzy Neural Network (HFNN). Generally in formal fuzzy neural networks (FNN), increasing the number of inputs, causes exponential growth in the number of parameters of the FNN system. This phenomenon named as" curse of dimensionality". An approach to deal with this problem is to use the hierarchical fuzzy neural network. A HFNN consists of hierarchically connected low-dimensional fuzzy neural networks. HFNN can use less rules to model nonlinear system. This method is applied to the Wisconsin Breast Cancer Database (WBCD) to classify breast cancer into two groups: benign and malignant lesions. The performance of HFNN is then compared with FNN by using the same breast cancer dataset.

This paper describes hybrid multivariate method: Principal Component Analysis improved by Genetic Algorithm. This method determines main Principal Components can be used to detect fault during the operation of industrial process by neural classifier. This technique is applied to simulated data collected from the Tennessee Eastman chemical plant simulator which was designed to simulate a wide variety of faults occurring in a chemical plant based on a facility at Eastman chemical.

This paper proposes a neural sliding mode control scheme for the synchronization of two chaotic nonlinear gyros subject to uncertainties and external disturbance. In this scheme, sliding mode control and multi layer perceptron neural network control are combined. A sliding surface is adopted to ensure the stability of the error dynamics in sliding mode control. The adaptation law of the multi layer perceptron neural network control system is derived in the sense of Lyapunov function, thus the system can be guaranteed to be asymptotically stable. By Lyapunov stability theory, neural sliding mode control is presented to ensure the stability of the controlled system. Multi layer perceptron Neural Network control is trained during the control process. The proposed method allows us to synchronize gyros by controlling the slave system. It is not necessary to calculate the Lyapunov exponents and the eigenvalues of the jacobian matrix, which makes it simple and convenient. Also, it is a systematic procedure for chaos synchronization of uncertain nonlinear gyro systems. Note that it needs only one controller to realize synchronization and the controller is easy to be implemented. The simulation results demonstrate the ability of the neural sliding control scheme to synchronize the chaotic gyro systems.

One of the most common cardiovascular diseases is Myocardial Ischemia (MI). The aim of this study is improving the diagnosis level of Ischemic Beat detection from ECG signals which is important in ischemic episode detection process. This improvement is based on appropriate feature extraction via Multi resolution Wavelet analysis and proper classifier selection. The approach starts with signal preprocessing, Afterwards efficacious morphologic features which are useful in ischemia detection are extracted by wavelet analysis. In the third stage subtractive clustering is performed for clustering. Finally probabilistic neural networks are used as a classifier. The proposed algorithm is evaluated on the European Society of Cardiology (ESC) ST-T database and reported 96.67% sensitivity and 89.18% specificity.

Monitoring and controlling the depth of anesthesia in surgery is so important. Compartmental models are well suited for closed-loop control of drug administration. In this paper, we develop a neural network and a fuzzy controller for nonlinear and compartmental system with nonnegative control input. In addition, the controllers guarantee that the physical system states remain in the nonnegative state space. After that, the proposed approaches are used to control the infusion of the anesthetic drug propofol in order to maintain a desired constant level of the depth of anesthesia for noncardiac surgery. In the end, this goal can be reached that intelligent systems are better than classic adaptive controller in adjustment of anesthesia with suitable condition of patient.

The alcoholism is one of psychiatric phenotype, which results from interplay between genetic and environmental factors. Not only it leads to brain defects but also associated cognitive, emotional, and behavioral impairments. It can be detected by analyzing EEG signals. In this research, the power spectrum of the Haar mother wavelet is extracted as features. Then the principle component analysis is applied for dimension reduction of the feature vectors. Finally support vectors machine and neural networks are used for classification. The simulation results show that our proposed method achieves better classification accuracy than the other methods.

Brain Computer Interface one of hopeful interface technologies between humans and machines. Electroencephalogram-based Brain Computer Interfaces have become a hot spot in the research of neural engineering, rehabilitation, and brain science. The artifacts are disturbance that can occur during the signal acquisition and that can alter the analysis of the signals themselves. Detecting artifacts produced in electroencephalography data by muscle activity, eye blinks and electrical noise is a common and important problem in electroencephalography research. In this research, we used five different methods for detecting trials containing artifacts. Finally we used two different neural networks, and support vector machine to classify features that are extracted by wavelet transform.

This paper proposes the generalized projective synchronization for chaotic systems via Gaussian Radial Basis Adaptive Backstepping Control. In the neural backstepping controller, a Gaussian radial basis function is utilized to on-line estimate the system dynamic function. The adaptation laws of the control system are derived in the sense of Lyapunov function, thus the system can be guaranteed to be asymptotically stable. The proposed method allows us to arbitrarily adjust the desired scaling by controlling the slave system. It is not necessary to calculate the Lyapunov exponents and the eigen values of the Jacobian matrix, which makes it simple and convenient. Also, it is a systematic procedure for generalized projective synchronization of chaotic systems and it can be applied to a variety of chaotic systems no matter whether it contains external excitation or not. Note that it needs only one controller to realize generalized projective synchronization no matter how much dimensions the chaotic system contains and the controller is easy to be implemented. The proposed method is applied to three chaotic systems: Genesio system, Rössler system, and Duffing system.

This paper suggests novel hybrid learning algorithm with stable learning laws for adaptive network based fuzzy inference system (ANFIS) as a system identifier and studies the stability of this algorithm. The new hybrid learning algorithm is based on particle swarm optimization (PSO) for training the antecedent part and gradient descent (GD) for training the conclusion part. Lyapunov stability theory is used to study the stability of the proposed algorithm. This paper, studies the stability of PSO as an optimizer in training the identifier, for the first time. Stable learning algorithms for the antecedent and consequent parts of fuzzy rules are proposed. Some constraints are obtained and simulation results are given to validate the results. It is shown that instability will not occur for the leaning rate and PSO factors in the presence of constraints. The learning rate can be calculated on-line and will provide an adaptive learning rate for the ANFIS structure. This new learning scheme employs adaptive learning rate that is determined by input–output data.

This paper suggests a novel approach for control of a flexible-link based on the feedback- error-learning (FEL) strategy. A radial basis function neural network (RBFNN) is used as an adaptive controller to improve the performance of a lead compensator controller in FEL structure. This scheme is developed by using a modified version of the FEL approach to learn the inverse dynamic of the flexible manipulator which requires only a linear model of the system for designing lead compensators and RBFNN controllers. The final controller should allow the user to command a desired tip angle position. The controller should eliminate the link's vibrations while maintaining a desirable level of response. Finally, the control performance of the proposed control approach for tip position tracking of flexible-link manipulator is illustrated by simulation result.

This paper proposes a novel hybrid learning algorithm with stable learning laws for Adaptive Network-based Fuzzy Inference System (ANFIS) as a system identifier. The proposed hybrid learning algorithm is based on the particle swarm optimization (PSO) for training the antecedent part and the extended Kalman filter (EKF) for training the conclusion part. Lyapunov stability theory is used to study the stability of the proposed algorithm. Comparison results of the proposed approach, PSO algorithm for training the antecedent part and recursive least squares (RLSs) or EKF algorithm for training the conclusion part, with the other classical approaches such as, gradient descent, resilient propagation, quick propagation, Levenberg–Marquardt for training the antecedent part and RLSs algorithm for training the conclusion part are provided. Moreover, it is shown that applying PSO, a powerful optimizer, to optimally train the parameters of the membership function on the antecedent part of the fuzzy rules in ANFIS system is a stable approach which results in an identifier with the best trained model. Stability constraints are obtained and different simulation results are given to validate the results. Also, the stability of Levenberg–Marquardt algorithms for ANFIS training is analyzed.

Brain Computer Interface (BCI) is a technology that developed over the last three decades has provided a novel and promising alternative method for interacting with the environment. BCI is a system which translates a subject's intentions into a control signal for a device, e.g., a computer application, a wheelchair or a neuroprosthesis. Electroencephalogram-based BCI has become a hot spot in the research of neural engineering, rehabilitation, and brain science. The artifacts are disturbance that can occur during the signal acquisition and that can alter the analysis of the signals themselves. Removing artifacts produced in Electroencephalogram (EEG) data by muscle activity, eye blinks and electrical noise is a common and important problem in EEG analysis. In this research, for artifact rejection, EEG data are filtered to the frequency range between 8 and 32 Hz with a butterworth band-pass filter. Finally two different structures of neural network and a support vector machine used to classify features that are extracted by Hilbert and Wavelet transform.

In the present research, a non-linear controller is designed for the control of an active suspension system for a half-model vehicle, using a Fuzzy Neural Network (FNN) along with Feedback error learning. The purpose in a vehicle suspension system is reduction of transmittance of vibrational effects from the road to the vehicle chassis, hence providing ride comfort. This requires a minimum reduction in road contact along rough roads. In addition, the role of the suspension system in vehicle control along a curved route and in accelerating and braking is quite evident. To accomplish this, one can first design a PD controller for the suspension system, using a classic control method and use it to train a fuzzy controller. This controller can be trained using the PD controller output error on an online manner. Once trained, the PD controller is removed from the control loop and the neuro-fuzzy controller takes on. In case of a change in the parameters of the system under control, the PD controller enters the control loop again and the neural network gets trained again for the new condition. Important characteristics of the proposed controller is that no mathematical model is needed for the system components, such as the non-linear actuator, spring, or shock absorber, and that no system Jacobian is needed. The performance of the proposed FNN controller is compared with that of the PD controller through simulations. The results show that the proposed controller is indeed capable of meeting the stated control requirements.

In this study, a new group method of data handling (GMDH) method, based on adaptive neurofuzzy inference system (ANFIS) structure, called ANFIS-GMDH and its application for diabetes mellitus forecasting is presented. Conventional neurofuzzy GMDH (NF-GMDH) uses radial basis network (RBF) as the partial descriptions. In this study the RBF partial descriptions are replaced with two input ANFIS structures and backpropagation algorithm is chosen for learning this network structure. The Prima Indians diabetes data set is used as training and testing sets which consist of 768 data whereby 268 of them are diagnosed with diabetes. The result of this study will provide solutions to the medical staff in determining whether someone is the diabetes sufferer or not which is much easier rather than currently doing a blood test. The results show that the proposed method performs better than the other models such as multi layer perceptron (MLP), RBF and ANFIS structure.

In this study integer genetic algorithm is applied for path planning of mobile robot in the grid form environment. The novel representation is proposed for definition of chromosome which reduced the computational complexity of genetic algorithm which was used before for path planning. Comparison with other encoding of chromosome is done to show the capability of proposed algorithm. Another genetic algorithm is used to repair some paths which collide with obstacles. Mamadani fuzzy rule is used to describe difficulty of passing from cells which are sandy or have slope.

In this paper designing of multi-objective PID controller for load frequency control (LFC) based on adaptive weighted particle swarm optimization (AWPSO) has been proposed. Conventional methods such as Ziegler-Nichols and Cohen-Coon are based on trial-and- error and their best performances are achieved for first-order process. Single-objective population based methods such as genetic algorithm (GA) and particle swarm optimization (PSO) have only one solution in a single run. Unlike single objective methods, multi- objective optimization can find different solutions in a single run. In the proposed method, overshoot/undershoot and settling time are used as objective functions for multi-objective optimization. The proposed method is used for designing of PID parameters for two area interconnected power system.

This paper introduces a new hybrid approach for training the adaptive network based fuzzy inference system (ANFIS) and a new type of particle swarm optimizers (PSO). The previous works emphasized on gradient base method or least square (LS) based method. This study applied one of the swarm intelligent branches, PSO. The hybrid method composes Fuzzy PSO with recursive least square (RLS) for training. We use PSO with some changes for training procedure parameters in antecedent part. These changes are inspired from Fuzzy Systems method and using Fuzzy rules for tuning PSO parameters during training algorithms. The simulation results show that in comparison with current gradient based training, and authors previous hybrid method the proposed training have a good adaptation to complex plants and train less parameter than gradient base methods.

This paper, describes a hybrid control method to control a flexible joint. Dynamic equation of the system has been derived. The designed controllers consist of two parts: classical controller, which is a Linear Quadratic Regulation (LQR), and a hybrid controller, utilizing sliding mode control using Gaussian Radial Basis Function Neural Networks (RBFNN). The RBFNN is trained during the control process and it is not necessary to be trained off-line.

This study suggests new learning laws for Adaptive Network based Fuzzy Inference System that is structured on the basis of TSK type III as a system identifier. Stable learning algorithms for consequence parts of TSK type III rules are proposed on the basis of the Lyapunov stability theory and some constraints are obtained. Simulation results are given to validate the results. It is shown that instability will not occur for learning rates in the presence of constraints. The learning rate can be calculated online from the input–output data, and an adaptive learning for the Adaptive Network based Fuzzy Inference System structure can be provided.

In this paper a decoupled sliding-mode with fuzzy neural network controller for a nonlinear system is presented. To divided into two subsystems to achieve asymptotic stability by decoupled method for a class of three order nonlinear system. The fuzzy neural network (FNN) is the main regulator controller, which is used to approximate an ideal computational controller. The compensation controller is designed to compensate for the difference between the ideal computational controller and the FNN controller. A tuning methodology is derived to update weight parts of the FNN. Using Lyapunov law, we derive the decoupled sliding-mode control law and the related parameters adaptive law of FNN. The method can control one-input and multi-output nonlinear systems efficiently. Using this approach, the response of system will converge faster than that of previous reports.

This paper present power system load frequency control by modified dynamic neural networks controller. The controller has dynamic neurons in hidden layer and conventional neurons in other layers. For considering the sensitivity of power system model, the neural network emulator used to identify the model simultaneously with control process. To have validation of proposed structure of neural network controller the results of simulation demonstrated that the proposed controller offers better performance than conventional neural network controller.

This paper introduces a new hybrid approach for training the adaptive network based fuzzy inference system (ANFIS). This approach based on multi objective optimization mechanism for training parameters in antecedent part. It considers two cost functions as the objectives which are the maximum difference measurements between the real nonlinear system and the nonlinear model, and training mean square error (MSE). The NSGA-II is the multi objective optimization algorithm which employed for this purpose. So we use gradient decent (GD) method for training all parameters in conclusion part. Finally we show simulation results of applied this method to some nonlinear identification system.

This paper presents sliding mode control of rotary inverted pendulum. Rotary inverted pendulum is a nonlinear, unstable and non-minimum-phase system. Designing sliding mode controller for such system is difficult in general. Here, first the desired performance is introduced and based on this performance two sliding surfaces are designed, then system is controlled by proper definition of a lyapunov function. The lyapunov function designed puts more emphasis on the control of the inverted pendulum rather than the control of the motor.

This study addresses new hybrid approaches for velocity control of an electro hydraulic servosystem (EHSS) in presence of flow nonlinearities and internal friction. In our new approaches, we combined classical method based-on sliding mode control and fuzzy RBF networks. The control by using adaptive networks need plant's Jacobean, but here this problem solved by sliding surface. It is demonstrated that this new technique have good ability control performance. It is shown that this technique can be successfully used to stabilize any chosen operating point of the system. All derived results are validated by computer simulation of a nonlinear mathematical model of the system. The controllers which introduced have big range for control the system.

This paper has introduced a new method for feature subset selection to which less attention has been given. Most of the past works have emphasized feature extraction and classification using classical methods for these works. The main goal in feature extraction is presented data in lower dimension. One of the popular methods in feature extraction is principle component analysis (PCA). This method and similar methods rely mostly on powerful classification algorithms to deal with redundant and irrelevant features. In this paper we introduced particle swarm optimization (PSO) as a simple, general, and powerful framework for selecting good subsets of features, leading to improved detection rates. We used PCA for feature extraction and support vector machines (SVMs) for classification. The goal is to search the PCA space using PSO to select a subset of eigenvectors encoding important information about the target concept of interest. Another object in this paper is to increase speed of convergence by using PSO to find the best feature. We have tested the framework in mind on challenging application like face detection. Our results illustrate the significant improvement in this case